Goal Representations for Instruction Following – The Berkeley Artificial Intelligence Research Blog


Goal Representations for Instruction Following

A longstanding aim of the sphere of robotic studying has been to create generalist brokers that may carry out duties for people. Natural language has the potential to be an easy-to-use interface for people to specify arbitrary duties, however it’s tough to coach robots to comply with language directions. Approaches like language-conditioned behavioral cloning (LCBC) practice insurance policies to instantly imitate skilled actions conditioned on language, however require people to annotate all coaching trajectories and generalize poorly throughout scenes and behaviors. Meanwhile, current goal-conditioned approaches carry out a lot better at normal manipulation duties, however don’t allow straightforward process specification for human operators. How can we reconcile the benefit of specifying duties by LCBC-like approaches with the efficiency enhancements of goal-conditioned studying?

Conceptually, an instruction-following robotic requires two capabilities. It must floor the language instruction within the bodily atmosphere, after which be capable of perform a sequence of actions to finish the supposed process. These capabilities don’t must be realized end-to-end from human-annotated trajectories alone, however can as a substitute be realized individually from the suitable information sources. Vision-language information from non-robot sources will help be taught language grounding with generalization to numerous directions and visible scenes. Meanwhile, unlabeled robotic trajectories can be utilized to coach a robotic to succeed in particular aim states, even when they don’t seem to be related to language directions.

Conditioning on visible targets (i.e. aim pictures) supplies complementary advantages for coverage studying. As a type of process specification, targets are fascinating for scaling as a result of they are often freely generated hindsight relabeling (any state reached alongside a trajectory generally is a aim). This permits insurance policies to be educated by way of goal-conditioned behavioral cloning (GCBC) on massive quantities of unannotated and unstructured trajectory information, together with information collected autonomously by the robotic itself. Goals are additionally simpler to floor since, as pictures, they are often instantly in contrast pixel-by-pixel with different states.

However, targets are much less intuitive for human customers than pure language. In most instances, it’s simpler for a person to explain the duty they need carried out than it’s to supply a aim picture, which might possible require performing the duty anyhow to generate the picture. By exposing a language interface for goal-conditioned insurance policies, we will mix the strengths of each goal- and language- process specification to allow generalist robots that may be simply commanded. Our methodology, mentioned beneath, exposes such an interface to generalize to numerous directions and scenes utilizing vision-language information, and enhance its bodily expertise by digesting massive unstructured robotic datasets.

Goal Representations for Instruction Following

diagram illustrating the overall approach of GRIF

The GRIF mannequin consists of a language encoder, a aim encoder, and a coverage community. The encoders respectively map language directions and aim pictures right into a shared process illustration area, which circumstances the coverage community when predicting actions. The mannequin can successfully be conditioned on both language directions or aim pictures to foretell actions, however we’re primarily utilizing goal-conditioned coaching as a means to enhance the language-conditioned use case.

Our strategy, Goal Representations for Instruction Following (GRIF), collectively trains a language- and a goal- conditioned coverage with aligned process representations. Our key perception is that these representations, aligned throughout language and aim modalities, allow us to successfully mix the advantages of goal-conditioned studying with a language-conditioned coverage. The realized insurance policies are then capable of generalize throughout language and scenes after coaching on largely unlabeled demonstration information.

We educated GRIF on a model of the Bridge-v2 dataset containing 7k labeled demonstration trajectories and 47k unlabeled ones inside a kitchen manipulation setting. Since all of the trajectories on this dataset needed to be manually annotated by people, with the ability to instantly use the 47k trajectories with out annotation considerably improves effectivity.

To be taught from each sorts of information, GRIF is educated collectively with language-conditioned behavioral cloning (LCBC) and goal-conditioned behavioral cloning (GCBC). The labeled dataset accommodates each language and aim process specs, so we use it to oversee each the language- and goal-conditioned predictions (i.e. LCBC and GCBC). The unlabeled dataset accommodates solely targets and is used for GCBC. The distinction between LCBC and GCBC is only a matter of choosing the duty illustration from the corresponding encoder, which is handed right into a shared coverage community to foretell actions.

By sharing the coverage community, we will count on some enchancment from utilizing the unlabeled dataset for goal-conditioned coaching. However,GRIF allows a lot stronger switch between the 2 modalities by recognizing that some language directions and aim pictures specify the identical conduct. In specific, we exploit this construction by requiring that language- and goal- representations be comparable for a similar semantic process. Assuming this construction holds, unlabeled information may profit the language-conditioned coverage because the aim illustration approximates that of the lacking instruction.

Alignment by Contrastive Learning

diagram illustrating the contrastive objective

We explicitly align representations between goal-conditioned and language-conditioned duties on the labeled dataset by contrastive studying.

Since language usually describes relative change, we select to align representations of state-goal pairs with the language instruction (versus simply aim with language). Empirically, this additionally makes the representations simpler to be taught since they’ll omit most data within the pictures and concentrate on the change from state to aim.

We be taught this alignment construction by an infoNCE goal on directions and pictures from the labeled dataset. We practice twin picture and textual content encoders by doing contrastive studying on matching pairs of language and aim representations. The goal encourages excessive similarity between representations of the identical process and low similarity for others, the place the detrimental examples are sampled from different trajectories.

When utilizing naive detrimental sampling (uniform from the remainder of the dataset), the realized representations usually ignored the precise process and easily aligned directions and targets that referred to the identical scenes. To use the coverage in the actual world, it’s not very helpful to affiliate language with a scene; fairly we want it to disambiguate between totally different duties in the identical scene. Thus, we use a tough detrimental sampling technique, the place as much as half the negatives are sampled from totally different trajectories in the identical scene.

Naturally, this contrastive studying setup teases at pre-trained vision-language fashions like CLIP. They reveal efficient zero-shot and few-shot generalization functionality for vision-language duties, and provide a strategy to incorporate data from internet-scale pre-training. However, most vision-language fashions are designed for aligning a single static picture with its caption with out the power to grasp adjustments within the atmosphere, they usually carry out poorly when having to concentrate to a single object in cluttered scenes.

To deal with these points, we devise a mechanism to accommodate and fine-tune CLIP for aligning process representations. We modify the CLIP structure in order that it may function on a pair of pictures mixed with early fusion (stacked channel-wise). This seems to be a succesful initialization for encoding pairs of state and aim pictures, and one which is especially good at preserving the pre-training advantages from CLIP.

Robot Policy Results

For our most important outcome, we consider the GRIF coverage in the actual world on 15 duties throughout 3 scenes. The directions are chosen to be a mixture of ones which can be well-represented within the coaching information and novel ones that require some extent of compositional generalization. One of the scenes additionally options an unseen mixture of objects.

We examine GRIF towards plain LCBC and stronger baselines impressed by prior work like LangLfP and BC-Z. LLfP corresponds to collectively coaching with LCBC and GCBC. BC-Z is an adaptation of the namesake methodology to our setting, the place we practice on LCBC, GCBC, and a easy alignment time period. It optimizes the cosine distance loss between the duty representations and doesn’t use image-language pre-training.

The insurance policies have been prone to 2 most important failure modes. They can fail to grasp the language instruction, which leads to them making an attempt one other process or performing no helpful actions in any respect. When language grounding shouldn’t be sturdy, insurance policies may even begin an unintended process after having performed the proper process, because the authentic instruction is out of context.

Examples of grounding failures

grounding failure 1

“put the mushroom within the metallic pot”

grounding failure 2

“put the spoon on the towel”

grounding failure 3

“put the yellow bell pepper on the fabric”

grounding failure 4

“put the yellow bell pepper on the fabric”

The different failure mode is failing to control objects. This could be as a result of lacking a grasp, shifting imprecisely, or releasing objects on the incorrect time. We observe that these usually are not inherent shortcomings of the robotic setup, as a GCBC coverage educated on your entire dataset can persistently achieve manipulation. Rather, this failure mode usually signifies an ineffectiveness in leveraging goal-conditioned information.

Examples of manipulation failures

manipulation failure 1

“transfer the bell pepper to the left of the desk”

manipulation failure 2

“put the bell pepper within the pan”

manipulation failure 3

“transfer the towel subsequent to the microwave”

Comparing the baselines, they every suffered from these two failure modes to totally different extents. LCBC depends solely on the small labeled trajectory dataset, and its poor manipulation functionality prevents it from finishing any duties. LLfP collectively trains the coverage on labeled and unlabeled information and exhibits considerably improved manipulation functionality from LCBC. It achieves cheap success charges for widespread directions, however fails to floor extra advanced directions. BC-Z’s alignment technique additionally improves manipulation functionality, possible as a result of alignment improves the switch between modalities. However, with out exterior vision-language information sources, it nonetheless struggles to generalize to new directions.

GRIF exhibits one of the best generalization whereas additionally having robust manipulation capabilities. It is ready to floor the language directions and perform the duty even when many distinct duties are potential within the scene. We present some rollouts and the corresponding directions beneath.

Policy Rollouts from GRIF

rollout 1

“transfer the pan to the entrance”

rollout 2

“put the bell pepper within the pan”

rollout 3

“put the knife on the purple material”

rollout 4

“put the spoon on the towel”


GRIF allows a robotic to make the most of massive quantities of unlabeled trajectory information to be taught goal-conditioned insurance policies, whereas offering a “language interface” to those insurance policies by way of aligned language-goal process representations. In distinction to prior language-image alignment strategies, our representations align adjustments in state to language, which we present results in important enhancements over commonplace CLIP-style image-language alignment aims. Our experiments reveal that our strategy can successfully leverage unlabeled robotic trajectories, with massive enhancements in efficiency over baselines and strategies that solely use the language-annotated information

Our methodology has plenty of limitations that could possibly be addressed in future work. GRIF shouldn’t be well-suited for duties the place directions say extra about methods to do the duty than what to do (e.g., “pour the water slowly”)—such qualitative directions may require different sorts of alignment losses that contemplate the intermediate steps of process execution. GRIF additionally assumes that each one language grounding comes from the portion of our dataset that’s absolutely annotated or a pre-trained VLM. An thrilling route for future work can be to increase our alignment loss to make the most of human video information to be taught wealthy semantics from Internet-scale information. Such an strategy might then use this information to enhance grounding on language exterior the robotic dataset and allow broadly generalizable robotic insurance policies that may comply with person directions.

This submit relies on the next paper:

If GRIF evokes your work, please cite it with:

      title={Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control},
      creator={Vivek Myers and Andre He and Kuan Fang and Homer Walke and Philippe Hansen-Estruch and Ching-An Cheng and Mihai Jalobeanu and Andrey Kolobov and Anca Dragan and Sergey Levine},
      12 months={2023},


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